Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

By Vito Bellini, Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio. 2017

31 citations recorded by the source corpus.

In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments, we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weight semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behaves in the presence of a few ratings from a user on the Movielens 1M dataset and compare results with BPRSLIM.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, methodology): “In a semantics-aware autoencoder, the hidden layers and their connections are substituted by the knowledge graph thus having an explicit representation on the meaning associated both to hidden nodes and to their mutual connections [2].”
Computing Recommendations via a Knowledge Graph-aware Autoencoder (2018, methodology): “Some of the ideas presented in this work have been originally presented in [2] where an analogous approach has been adopted for the first time to tackle the cold start problem.”
A review on deep learning for recommender systems: challenges and remedies (2018, methodology): “2017; Bellini et al. 2017; Li and She 2017). Strub et al. (2016) integrate side information into each layer of the DAE. Gunawardana and Meek (2008) tie Boltzmann machines to content information of items to handle the cold-start problem. Kim et al. (2017) utilize CNN to extract features from images a”